AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE)

未来边缘网络和分布式智能人工智能研究所 (AI-EDGE)

• 批准号: 2112471
• 负责人: Ness Shroff
• 金额: $ 1999.06万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112471&HistoricalAwards=false
• 关键词: AI; Institute; Future; Edge; Networks

Networking and AI are two of the most transformative IT technologies --- helping to better people’s lives, contributing to national economic competitiveness, national security, and national defense. The Institute will exploit the synergies between networking and AI to design the next generation of edge networks (6G and beyond) that are highly efficient, reliable, robust, and secure. A new distributed intelligence plane will be developed to ensure that these networks are self-healing, adaptive, and self-optimized. The future of AI is distributed because AI will increasingly be implemented across a diverse set of edge devices. These intelligent and adaptive networks will in turn unleash the power of collaboration to solve long-standing distributed AI challenges, making AI more efficient, interactive, and privacy-preserving. The Institute will develop the key underlying technologies for distributed and networked intelligence to enable a host of future transformative applications such as intelligent transportation, remote healthcare, distributed robotics, and smart aerospace. It is a national priority to educate students, professionals, and practitioners in AI and networks, and substantially grow and diversify the workforce. The Institute will develop novel, efficient, and modular ways of creating and delivering educational content and curricula at scale, and to spearhead a program that helps build a large diverse workforce in AI and networks spanning K-12 to university students and faculty.The focus of the AI Institute will be on edge networks, which will constitute the majority of the growth of future networks. This edge includes all devices connected through the radio as well as data centers and cloud computing systems that are not at the core of the Internet. A critical component of the Institute is to shorten the time-scale of interactions between Foundations and use case research across multiple disciplines. This will result in a virtuous cycle that will have a cascading impact dramatically accelerating the time it takes from research to implementation and technology transfer. The research tasks will be enhanced and fleshed out by exploring three wireless edge use cases in depth: (1) Ubiquitous Sensing/Networking; (ii) Human-Machine Mobility and (iii) Programmable/virtualized 6G networks. These use cases are important in their own right and connect the key research thrusts and their validation to specific experimental platforms. The Institute will work with its industry and DoD partners to facilitate translation and adoption.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

网络和人工智能是两种最具变革性的IT技术--帮助改善人们的生活，为国家经济竞争力、国家安全和国防做出贡献。该研究所将利用网络和人工智能之间的协同作用，设计高效、可靠、强大和安全的下一代边缘网络（6 G及以上）。将开发一个新的分布式智能平面，以确保这些网络能够自我修复、自适应和自我优化。人工智能的未来是分布式的，因为人工智能将越来越多地在各种边缘设备上实现。这些智能和自适应的网络将反过来释放协作的力量，以解决长期存在的分布式人工智能挑战，使人工智能更加高效，互动和隐私保护。该研究所将开发分布式和网络智能的关键基础技术，以实现智能交通、远程医疗、分布式机器人和智能航空航天等一系列未来变革性应用。教育学生、专业人士和人工智能和网络从业者，并大幅增长和多样化劳动力是国家的优先事项。该研究所将开发新颖、高效和模块化的方式，大规模创建和交付教育内容和课程，并率先开展一项计划，帮助建立一个庞大的人工智能和网络（从K-12到大学生和教师）的多元化劳动力队伍。人工智能研究所的重点将是边缘网络，这将构成未来网络增长的大部分。这种边缘包括通过无线电连接的所有设备以及数据中心和云计算系统，这些系统不是互联网的核心。该研究所的一个关键组成部分是缩短跨多个学科的基础和用例研究之间的交互的时间尺度。这将导致一个良性循环，产生连锁效应，大大加快从研究到实施和技术转让的时间。研究任务将通过深入探索三个无线边缘用例来增强和充实：（1）无处不在的感知/网络;（ii）人机移动性和（iii）可编程/虚拟化6 G网络。这些用例本身就很重要，并将关键的研究重点及其验证与特定的实验平台联系起来。该研究所将与其工业界和国防部合作伙伴合作，以促进翻译和采用。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning

• 发表时间: 2021
• 作者: Xin Zhang;Zhuqing Liu;Jia Liu;Zhengyuan Zhu-;Songtao Lu

Provably Faster Algorithms for Bilevel Optimization

• 发表时间: 2021-06
• 作者: Junjie Yang;Kaiyi Ji;Yingbin Liang

Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning

• 发表时间: 2021-06
• 作者: Tengyang Xie;Nan Jiang;Huan Wang;Caiming Xiong;Yu Bai

ARA: A Wireless Living Lab Vision for Smart and Connected Rural Communities

ARA：智能互联农村社区的无线生活实验室愿景

• DOI: 10.1145/3477086.3480837
• 发表时间: 2021
• 期刊: WINTECH 2021
• 作者: Zhang, Hongwei;Guan, Yong;Kamal, Ahmed;Qiao, Daji;Zheng, Mai;Arora, Anish;Boyraz, Ozdal;Cox, Brian;Daniels, Thomas;Darr, Matthew
• 通讯作者: Darr, Matthew

Robust and Differentially Private Mean Estimation

• 发表时间: 2021-02
• 作者: Xiyang Liu;Weihao Kong;S. Kakade;Sewoong Oh